Changes Over Time in Hepatic Markers Predict Changes in Insulin Sensitivity, β-Cell Function, and Glycemia
Bibliographic record
Abstract
Context: Serum concentrations of liver enzymes and the hepatokine fetuin-A have been linked to the risk of type 2 diabetes, but their longitudinal impact on insulin resistance and β-cell dysfunction is unclear. Objective: To evaluate the impact of changes over 2 years in fetuin-A and the liver enzymes alanine aminotransferase (ALT), aspartate aminotransferase (AST), and γ-glutamyltransferase (GGT) on changes in insulin sensitivity, β-cell function, and glycemia in women with varying degrees of previous gestational dysglycemia, reflecting a range of future diabetic risk. Design/Setting/Participants: In total, 336 women underwent glucose challenge test (GCT) and oral glucose tolerance test (OGTT) in pregnancy, followed by repeat OGTT and measurement of ALT/AST/GGT/fetuin-A at both 1 year and 3 years postpartum. The antepartum GCT/OGTT identified four gestational glucose tolerance groups: gestational diabetes (n = 104), gestational impaired glucose tolerance (n = 59), abnormal GCT with normal OGTT (n = 98), and normal GCT/OGTT (n = 75). Results: At 1 and 3 years postpartum, ALT, AST, GGT, and fetuin-A did not differ across the four groups, but the intervening change in ALT/AST ratio was greater in the gestational dysglycemia groups (P = 0.05). Higher baseline ALT/AST (t = -1.99, P = 0.05) and fetuin-A (t = -3.17, P = 0.002) predicted lower insulin sensitivity (Matsuda) at 3 years, as did their respective changes from 1 to 3 years (ALT/AST: t = -5.47, P < 0.0001; fetuin-A: t = -3.56, P = 0.0004). Change in ALT/AST predicted lower β-cell function (t = -2.33, P = 0.02) and higher fasting glucose at 3 years (t = 2.55, P = 0.01). Moreover, baseline fetuin-A predicted prediabetes/diabetes at 3 years (OR, 1.38; 95% CI, 1.01 to 1.88). Conclusion: Circulating hepatic markers, particularly ALT/AST ratio and fetuin-A, track with changes in insulin sensitivity and β-cell function, supporting a pathophysiologic basis in their prediction of diabetic risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".